  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. Despite increasing interest in UE, challenges
persist, such as the need for explicit methods to capture aleatoric uncertainty
and align uncertainty estimates with real-life disagreements among domain
experts. This paper proposes an Expert Disagreement-Guided Uncertainty
Estimation (EDUE) for medical image segmentation. By leveraging variability in
ground-truth annotations from multiple raters, we guide the model during
training and incorporate random sampling-based strategies to enhance
calibration confidence. Our method achieves 55% and 23% improvement in
correlation on average with expert disagreements at the image and pixel levels,
respectively, better calibration, and competitive segmentation performance
compared to the state-of-the-art deep ensembles, requiring only a single
forward pass.
