At Desupervised we have put a lot of effort into developing tooling such as Borch
that allows us to build models that have a proper uncertainty estimation. Uncertainty comes in very handy when working in high-stakes situations, say one wants to use image classification to determine if an x-ray scan shows signs of cancer. In that situation, you want a model that clearly shows when it is uncertain about a prediction such that you can have the scan reviewed by a doctor. As the consequences of a false negative could literally be the patient's life.