<p>To estimate and perform statistical inference on the total, direct and indirect effects of exposure and mediator variables with measurement error while accounting for high-dimensional confounders, we formulate a high-dimensional mediation model with measurement error. To simultaneously mitigate bias from measurement error and adjust for high-dimensional nuisance parameters related to confounders, two doubly debiased score functions are constructed for the total and direct effects based on the error-corrected loss functions and decorrelated score functions. We establish the asymptotic expressions and distributions of the three effect estimators and develop consistent estimators of their asymptotic covariance matrices. The satisfactory performance of our estimators is demonstrated through simulation studies, and an application to a real-world air pollution dataset further confirms their practical utility.</p>