The recent literature has shown that, with carefully selected training data sets and physical expertise (domain knowledge), the resulting ML predictive models allow for important discoveries in materials science. However, unleashing the full potential of data-centric approaches and fulfilling their promise to deliver results of ab initio quality requires that the uncertainty of the predictions be quantified. This UQ needs to be robust and reliable, and the related algorithm should be relatively straightforward to implement, such that users have a transparent access to it.