Complex models of blood flow in stenosed arteries can be used to patient-specifically predict outcome metrics, thereby supporting the physicians in decision making processes. However, these models are time consuming which limits the feasibility of output uncertainty quantification. Accurate surrogates (metamodels) might be the solution. In this study, we aim to demonstrate the feasibility of a generalized polynomial chaos expansion (gPCE)-based metamodel to predict a clinically relevant output metric and to quantify output uncertainty. As an example, a metamodel was constructed from a 2D CFD model that was shown to be able to estimate translesional pressure drops in iliac artery stenoses (-0.9±12.7mmHg, R2=0.81). The metamodel was constructed from a virtual database using the adaptive gPCE method. The constructed metamodel was then applied to 25 stenosed iliac arteries to predict the patient-specific pressure drop and to perform uncertainty quantification. Comparing predicted pressure drops of the metamodel and in vivo measured pressure drops, the mean bias (-0.2±13.7mmHg) and the coefficient of determination (R2=0.80) were as good as of the original 2D model. Uncertainty quantification results of the 2D and metamodel were comparable. Estimation of the uncertainty interval using the original 2D model took 14 days, whereas the result of the metamodel was instantly available. In conclusion, it is feasible to quantify the uncertainty of the output metric and perform sensitivity analysis instantly using a metamodel. Future studies should investigate the possibility to construct a metamodel of more complex problems.