医学图像的分割本质上是不明确的。捕捉病变分割的不确定性对于协助癌症诊断和进一步干预至关重要。最近的工作在生成多个看似合理的分割结果作为多样化参考以解释病变分割的不确定性方面取得了巨大进展。然而，现有模型的效率有限，并且多注释数据集中的不确定性信息仍有待充分利用。在本研究中，我们提出了一系列方法来共同解决上述限制，并利用多注释数据集中的丰富信息：（1）定制T时间内部采样网络，以提高建模灵活性并有效生成与地面匹配的样本- 多个注释者的真实分布； （2）定义不确定度，从全新的角度定量衡量每个样本的不确定性以及整个多标注数据集的不平衡性； (3) 不确定性感知数据增强策略，帮助概率模型自适应地拟合具有不同不确定性范围的样本。我们在公开的肺结节数据集和我们内部的肝脏肿瘤数据集上对每个数据集进行了评估。结果表明，我们提出的方法在准确性和效率上均实现了整体最佳性能，展示了其在实际临床场景中的病灶分割和更多下游任务中的巨大潜力。版权所有 © 2024 作者。由爱思唯尔有限公司出版。保留所有权利。

Segmentation in medical images is inherently ambiguous. It is crucial to capture the uncertainty in lesion segmentations to assist cancer diagnosis and further interventions. Recent works have made great progress in generating multiple plausible segmentation results as diversified references to account for the uncertainty in lesion segmentations. However, the efficiency of existing models is limited, and the uncertainty information lying in multi-annotated datasets remains to be fully utilized. In this study, we propose a series of methods to corporately deal with the above limitation and leverage the abundant information in multi-annotated datasets: (1) Customized T-time Inner Sampling Network to promote the modeling flexibility and efficiently generate samples matching the ground-truth distribution of a number of annotators; (2) Uncertainty Degree defined for quantitatively measuring the uncertainty of each sample and the imbalance of the whole multi-annotated dataset from a brand new perspective; (3) Uncertainty-aware Data Augmentation Strategy to help probabilistic models adaptively fit samples with different ranges of uncertainty. We have evaluated each of them on both the publicly available lung nodule dataset and our in-house Liver Tumor dataset. Results show that our proposed methods achieves the overall best performance on both accuracy and efficiency, demonstrating its great potential in lesion segmentations and more downstream tasks in real clinical scenarios.Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.