In this paper, we propose a rehearsal-based continual learning method that incorporates Monte Carlo dropout to measure uncertainty and store representative samples in the memory buffer. Unlike traditional learning methods, our approach dynamically updates the model with new information while retaining crucial past data. By leveraging Monte Carlo dropout to estimate uncertainty, our method selectively retains high-uncertainty data points, enhancing information retention and reducing redundancy.