In safety-critical applications like medical diagnosis, certainty associated with a model's prediction is just as important as its accuracy. Consequently, uncertainty estimation and reduction play a crucial role. Uncertainty in predictions can be attributed to noise or randomness in data (aleatoric) and incorrect model inferences (epistemic). While model uncertainty can be reduced with more data or bigger models, aleatoric uncertainty is more intricate. This work proposes a novel approach that interprets data uncertainty estimated from a self-supervised task as noise inherent to the data and utilizes it to reduce aleatoric uncertainty in another task related to the same dataset via data augmentation. The proposed method was evaluated on a benchmark medical imaging dataset with image reconstruction as the self-supervised task and segmentation as the image analysis task. Our findings demonstrate the effectiveness of the proposed approach in significantly reducing the aleatoric uncertainty in the image segmentation task while achieving better or on-par performance compared to the standard augmentation techniques.

翻译：在诸如医学诊断等安全关键应用中,与模型预测相关的确定性与其准确性一样重要。因此,不确定性估计和减少具有关键作用。预测中的不确定性可归因于数据(代号)中的噪音或随机性和不正确的模型推理(流行性)。虽然模型不确定性可以通过更多的数据或更大的模型来减少,但偏移性不确定性则更为复杂。这项工作提出了一个新颖的方法,将自监督任务中估算的数据不确定性解释为数据固有的噪音,并利用它来减少通过数据增强与同一数据集有关的另一项任务中的疏漏性不确定性。对拟议方法进行了评估,其基准医疗成像数据集进行了重建,其图像重建与图像分析任务一样,以自我监督任务和分割为特征。我们的调查结果表明拟议方法在大幅降低图像分割任务中的偏移性不确定性方面的有效性,同时取得与标准增强技术相比更好的或平行性性能。