Lesion segmentation is inherently influenced by imaging uncertainty, arising from ill-defined lesion boundaries and inter-observer variability in diagnosis. To address this challenge, previous works formulated the multi-rater medical image segmentation task, where multiple experts provide separate annotations for each image. However, existing models are typically constrained to either generate diverse segmentation that lacks expert specificity or to produce personalized outputs that merely replicate individual annotators. We propose \textbf{Pro}babilistic modeling of multi-rater lesion \textbf{Seg}mentation (\textbf{ProSeg}) that simultaneously enables both diversification and personalization. Specifically, we introduce two latent variables to model expert annotation preferences and lesion boundary ambiguity. Their conditional probabilistic distributions are then obtained through variational inference, allowing segmentation outputs to be generated by sampling from these distributions. Extensive experiments on both the nasopharyngeal carcinoma dataset (NPC) and the lung nodule dataset (LIDC-IDRI) demonstrate that our ProSeg achieves a new state-of-the-art performance, providing segmentation results that are both diverse and expert-personalized.

翻译：病变分割本质上受到成像不确定性的影响，这些不确定性源于病灶边界模糊以及诊断中观察者间的变异。为解决这一挑战，先前的工作制定了多评分者医学图像分割任务，即由多位专家为每幅图像提供独立的标注。然而，现有模型通常局限于生成缺乏专家特异性的多样化分割，或仅能复刻个体标注者的个性化输出。我们提出多评分者病变分割的概率建模方法（ProSeg），该方法能够同时实现多样化和个性化。具体而言，我们引入两个潜在变量来建模专家标注偏好和病灶边界模糊性，并通过变分推断获得它们的条件概率分布，从而通过从这些分布中采样生成分割输出。在鼻咽癌数据集（NPC）和肺结节数据集（LIDC-IDRI）上的大量实验表明，我们的ProSeg达到了最新的最优性能，能够提供既多样化又具备专家个性化的分割结果。