The accurate estimation of predictive uncertainty carries importance in medical scenarios such as lung node segmentation. Unfortunately, most existing works on predictive uncertainty do not return calibrated uncertainty estimates, which could be used in practice. In this work we exploit multi-grader annotation variability as a source of 'groundtruth' aleatoric uncertainty, which can be treated as a target in a supervised learning problem. We combine this groundtruth uncertainty with a Probabilistic U-Net and test on the LIDC-IDRI lung nodule CT dataset and MICCAI2012 prostate MRI dataset. We find that we are able to improve predictive uncertainty estimates. We also find that we can improve sample accuracy and sample diversity. In real-world applications, our method could inform doctors about the confidence of the segmentation results.

翻译：对预测性不确定性的准确估计在肺节点分割等医疗假设中具有重要意义。 不幸的是,大多数关于预测性不确定性的现有工程并不返回可实际使用的经校准的不确定性估计值。 在这项工作中,我们利用多梯度批注可变性作为“地面真实性”解析性不确定性的来源,这可以被视为受监督的学习问题的目标。我们将这种地面真实性不确定性与概率U-Net以及LIDDC-IDRI肺部结核CT数据集和MICCAI2012 Prostate MRI数据集的测试结合起来。我们发现,我们能够改进预测性不确定性估计值。我们还发现,我们可以改进样本的准确性和样本多样性。在现实世界的应用中,我们的方法可以让医生了解分解结果的信心。