LEXU: Learning from Expert Disagreement for Single-Pass Uncertainty Estimation in Medical Image Segmentation
Abutalip, Kudaibergen Saeed, Numan Ma’ani, Fadillah Adamsyah Sobirov, Ikboljon Andrearczyk, Vincent Depeursinge, Adrien Yaqub, Mohammad K.
Abutalip, Kudaibergen
Saeed, Numan
Ma’ani, Fadillah Adamsyah
Sobirov, Ikboljon
Andrearczyk, Vincent
Depeursinge, Adrien
Yaqub, Mohammad K.
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2026
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Deploying deep learning (DL) models in medical applications relies on predictive performance and other critical factors, such as conveying trustworthy predictive uncertainty. Uncertainty estimation (UE) methods provide potential solutions for evaluating prediction reliability and improving the model confidence calibration. This paper introduces Learning from EXpert Disagreement for UE (LEXU) for medical image segmentation, a method that leverages the variability in annotations from multiple experts to guide model training. By focusing on regions of disagreement among experts and incorporating multi-rater optimization strategy, LEXU enhances the model’s awareness of challenging cases, resulting in better calibration and predictive uncertainty. The method shows a 55% improvement in correlation with expert disagreements at the image level and a 23% improvement at the pixel level, along with competitive segmentation performance compared to state-of-the-art techniques, all while requiring only a single forward pass.
Citation
K. Abutalip et al., “LEXU: Learning from Expert Disagreement for Single-Pass Uncertainty Estimation in Medical Image Segmentation,” pp. 112–122, 2026, doi: 10.1007/978-3-032-06593-3_11
Source
Lecture Notes in Computer Science
Conference
7th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2025, held in conjunction with 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Keywords
Medical image segmentation, Model calibration, Uncertainty estimation
Subjects
Source
7th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2025, held in conjunction with 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Publisher
Springer Nature