Segmentation uncertainty models predict a distribution over plausible segmentations for a given input, which they learn from the annotator variation in the training set. However, in practice these annotations can differ systematically in the way they are generated, for example through the use of different labeling tools. This results in datasets that contain both data variability and differing label styles. In this paper, we demonstrate that applying state-of-the-art segmentation uncertainty models on such datasets can lead to model bias caused by the different label styles. We present an updated modelling objective conditioning on labeling style for aleatoric uncertainty estimation, and modify two state-of-the-art-architectures for segmentation uncertainty accordingly. We show with extensive experiments that this method reduces label style bias, while improving segmentation performance, increasing the applicability of segmentation uncertainty models in the wild. We curate two datasets, with annotations in different label styles, which we will make publicly available along with our code upon publication.

翻译：分割不确定性模型针对给定输入预测一组合理分割结果的分布，该分布通过训练集中的标注者差异学习获得。然而实际应用中，这些标注可能因生成方式（例如使用不同标注工具）存在系统性差异，导致数据集中同时包含数据变异性和不同标签风格。本文证明，在此类数据集上应用最先进的分割不确定性模型会引发由标签风格差异导致的模型偏差。我们提出一种基于标注风格条件化的认知不确定性估计更新建模目标，并据此修改了两种顶尖的分割不确定性架构。大量实验表明，该方法在提升分割性能的同时减少了标签风格偏差，增强了分割不确定性模型在实际场景中的适用性。我们整理了两个包含不同标签风格标注的数据集，将在论文发表后与代码一同公开发布。