DeferredSeg:A Multi-Expert Deferral Framework for Medical Image Segmentation

[2604.12411] DeferredSeg:A Multi-Expert Deferral Framework for Medical Image Segmentation Skip to main content Search Submit Donate Log in Computer Science > Computer Vision and Pattern Recognition arXiv:2604.12411 (cs) [Submitted on 14 Apr 2026 ( v1 ), last revised 28 Aug 2026 (this version, v2)] Title: DeferredSeg:A Multi-Expert Deferral Framework for Medical Image Segmentation Authors: Qiuyu Tian , Haoliang Sun , Yunshan Wang , Yinghuan Shi , Yilong Yin View PDF HTML (experimental) Abstract: Segmentation models based on deep neural networks demonstrate strong generalization for medical image segmentation. However, they often exhibit overconfidence or underconfidence, leading to unreliable confidence scores for segmentation masks, especially in ambiguous regions. This undermines the trustworthiness required for clinical deployment. Motivated by the learning-to-defer (L2D) paradigm, we introduce DeferredSeg, a deferral-aware segmentation framework, i.e., a Human--AI collaboration system that determines whether to defer predictions to human experts in specific regions. DeferredSeg extends the base segmentor with an aggregated deferral predictor and additional routing channels that dynamically route each pixel to either the base segmentor or a human expert. To train this routing efficiently, we introduce a pixel-wise surrogate collaboration loss that supervises deferral decisions. In addition, to preserve spatial coherence within deferral regions, we propose a spatial-coherence loss that enforces smooth deferral masks, thereby enhancing reliability. Beyond single-expert deferral, we further extend the framework to a multi-expert setting by introducing multiple discrepancy experts for collaborative decision-making. To prevent overloading or underutilizing individual experts, we further design a load-balancing penalty that evenly distributes workload across expert branches. We evaluate DeferredSeg on three challenging medical datasets using MedSAM and CENet as the base segmentor for fair comparison. Experimental results show that DeferredSeg consistently outperforms the baseline, demonstrating its effectiveness for trustworthy dense medical segmentation. Moreover, the proposed framework is model-agnostic and can be readily applied to other segmentation architectures. Comments: Accepted by Pattern Recognition Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2604.12411 [cs.CV] (or arXiv:2604.12411v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2604.12411 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Haoliang Sun [ view email ] [v1] Tue, 14 Apr 2026 07:50:31 UTC (7,168 KB) [v2] Fri, 28 Aug 2026 01:57:40 UTC (12,755 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CV < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• DeferredSeg:A Multi-Expert Deferral Framework for Medical Image Segmentation#682644
• Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection#280158
• CSIE: cancer subtyping via inference and ensemble.#262920
• U-Net: Convolutional Networks for Biomedical Image Segmentation#6253
• Label tree semantic losses for rich multi-class medical image segmentation.#330766