In optimization under uncertainty problems, estimating the statistical parameters that comprise the objective function and/or constraints many times during the search for the optimum can be computationally intensive. As a rst step towards multidelity optimization under uncertainty, uncertainty propagation through a computationally expensive, highdelity numerical model is considered. In many practical situations, low-delity models are available and can provide useful information about the outputs of the high-delity model at a lower cost. To take advantage of these low-delity models in a rigorous manner, this paper proposes a multidelity approach to Monte Carlo estimation of parameters such as the mean and the variance of the high-delity model outputs based on the control variate variance reduction method. Numerical experiments suggest that the multidelity approach is eective in reducing the variance of the estimator over regular Monte Carlo simulation for the same computational eort. When applied to evaluate the objective function of the acoustic horn robust optimization problem, multidelity Monte Carlo simulation demonstrated 80% lower computational cost.