When long-term follow up is required for a primary endpoint in a randomized clinical trial, a valid surrogate marker can help to estimate the treatment effect and accelerate the decision process. The Prentice criterion defines a valid surrogate marker by comparing a test for a treatment effect on the primary outcome versus a test for a treatment effect on the surrogate marker. Motivated by this criterion, several model-based methods have been developed to evaluate the proportion of the treatment effect that is explained by the treatment effect on the surrogate marker. More recently, a nonparametric approach has been proposed allowing for more flexibility by avoiding the restrictive parametric model assumptions required in the model-based methods. While the model-based approaches suffer from potential mis-specification of the models, the nonparametric method fails to give desirable estimates when the sample size is small, or when the range of the data does not follow certain conditions. In this paper, we propose a Bayesian model averaging approach to estimate the proportion of treatment effect explained by the surrogate marker. Our procedure offers a compromise between the model-based approach and the nonparametric approach by introducing model flexibility via averaging over several candidate models and maintains the strength of parametric models with respect to inference. We compare our approach with previous model-based methods and the nonparametric method. Simulation studies investigate the performance under cases that models are correctly specified and mis-specified. The results demonstrate the advantage of our method when surrogate supports are inconsistent and sample sizes are small, and our approach yields comparable performance in other scenarios. We illustrate our methos using a data set from diabetes study assessing treatment effect of lifestype intervention with a HbA1c surrogate marker.