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.

本研究提出了一种基于专家分歧引导的不确定性估计方法（EDUE），用于医学图像分割，通过利用多个评分者对地面事实标注的变异性，我们在训练过程中引导模型，并结合基于随机抽样的策略来增强校准置信度，我们的方法在图像和像素级别上与专家分歧相比，平均相关性分别提高了 55% 和 23%，具有更好的校准性能，并且相较于最先进的深度集合，在只需进行单次前向传递的情况下，具有具有竞争性的分割性能。

EDUE：基于专家意见引导的一遍式医学图像分割不确定性估计

EDUE: Expert Disagreement-Guided One-Pass Uncertainty Estimation for  Medical Image Segmentation

The issue of disagreements amongst human experts is a ubiquitous one in both
machine learning and medicine. In medicine, this often corresponds to doctor
disagreements on a patient diagnosis. In this work, we show that machine
learning models can be trained to give uncertainty scores to data instances
that might result in high expert disagreements. In particular, they can
identify patient cases that would benefit most from a medical second opinion.
Our central methodological finding is that Direct Uncertainty Prediction (DUP),
training a model to predict an uncertainty score directly from the raw patient
features, works better than Uncertainty Via Classification, the two-step
process of training a classifier and postprocessing the output distribution to
give an uncertainty score. We show this both with a theoretical result, and on
extensive evaluations on a large scale medical imaging application.

该研究表明，在医学领域中，机器学习模型可以通过训练对数据实例输出不确定性得分，以便针对存在高度医学专家分歧的情况，特别是可以确定应该获得医学二次意见的病例，和通过直接预测不确定性得分的方法（DUP），可以更好地进行训练，优于通过分类来预测不确定性分数的方法。这一结论得到了理论结果和在大规模医学影像应用中广泛评估的支持。