This has been the basis of overcoming domain-shift using many self-training techniques which select the most confident examples as new training examples, together with the predicted class as pseudo-label (McClosky et al., 2006).
modern neural networks are known to give wrongly calibrated confidence scores (Guo et al., 2017) meaning that the associated probability scores to the predicted class label does not reflect its correctness likelihood.