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The accurate evaluation of molecular properties lies at the core of predictive physical models. Most reliable quantum-chemical calculations are limited to smaller molecular systems while purely empirical approaches are limited in accuracy and reliability. A promising approach is to employ a quantum-mechanical formalism with simplifications and to compensate for the latter with parametrization. We propose a strategy of directly predicting the uncertainty interval for a property of interest, based on training-data uncertainties, which sidesteps the need for an optimum set of parameters.