A new family of metrics called $ECUAS_n$ has been proposed for evaluating uncertainty-augmented systems in automated decision-making, addressing the inadequacies of current assessment methods. These metrics allow for a tailored trade-off between the costs of incorrect predictions and imperfect uncertainties, enhancing decision-making under uncertainty.