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.

翻译：在医疗应用中部署深度学习模型不仅依赖于预测性能，还依赖于其他关键因素，例如传达可信的预测不确定性。不确定性估计方法为评估预测可靠性和改进模型置信度校准提供了潜在解决方案。尽管人们对不确定性估计的兴趣日益增加，但仍存在挑战，例如需要明确的方法来捕捉偶然不确定性并使不确定性估计与领域专家之间的现实分歧保持一致。本文提出了一种专家分歧引导的不确定性估计方法（EDUE），用于医学图像分割。通过利用来自多个评分者的真实标注变异性，我们在训练过程中引导模型，并引入基于随机抽样的策略来增强校准置信度。与最先进的深度集成方法相比，我们的方法在图像和像素级别上分别与专家分歧的平均相关性提高了55%和23%，实现了更好的校准效果以及具有竞争力的分割性能，且仅需单次前向传播。