Medical image segmentation modeling is a high-stakes task where understanding of uncertainty is crucial for addressing visual ambiguity. Prior work has developed segmentation models utilizing probabilistic or generative mechanisms to infer uncertainty from labels where annotators draw a singular boundary. However, as these annotations cannot represent an individual annotator's uncertainty, models trained on them produce uncertainty maps that are difficult to interpret. We propose a novel segmentation representation, Confidence Contours, which uses high- and low-confidence ``contours'' to capture uncertainty directly, and develop a novel annotation system for collecting contours. We conduct an evaluation on the Lung Image Dataset Consortium (LIDC) and a synthetic dataset. From an annotation study with 30 participants, results show that Confidence Contours provide high representative capacity without considerably higher annotator effort. We also find that general-purpose segmentation models can learn Confidence Contours at the same performance level as standard singular annotations. Finally, from interviews with 5 medical experts, we find that Confidence Contour maps are more interpretable than Bayesian maps due to representation of structural uncertainty.

翻译：医学图像分割建模是一项高风险任务，理解不确定性对于解决视觉歧义至关重要。先前的研究开发了利用概率或生成机制的分割模型，从标注者绘制单一边界标签中推断不确定性。然而，由于这些标注无法代表单个标注者的不确定性，基于它们训练的模型所产生的不确定性图谱难以解释。我们提出一种新颖的分割表示方法——置信轮廓，它通过高置信度和低置信度的"轮廓"直接捕捉不确定性，并开发了一套用于收集轮廓的新型标注系统。我们在肺部图像数据集联盟（LIDC）和一个合成数据集上进行了评估。一项包含30名参与者的标注研究表明，置信轮廓能在不显著增加标注者工作量的情况下提供高表征能力。我们还发现，通用分割模型能够以与标准单一标注相同的性能水平学习置信轮廓。最后，通过对5名医学专家的访谈，我们发现由于能表征结构性不确定性，置信轮廓图谱比贝叶斯图谱更具可解释性。