  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.
