Although reliability has to be prioritised, any UQ algorithm must not add a substantial computational cost to the ML model it is being applied to, since in materials modelling efficiency is often a core requirement to achieve meaningful simulations. This observation applies both to the realm of surrogate modelling where, e.g., millions of force evaluations with uncertainty quantification need to be carried out, and to the realm of materials genomics where, e.g., millions of candidate systems need to be classified including this quantification. Achieving such a framework requires the community to adopt more widespread standards and work together on benchmarking efforts targeted at error prediction.