In embedded systems, tightly coupled memories (TCMs) are usually shared between multiple masters for the purpose of performance scalability, hardware efficiency and software flexibility. On the one hand, memory sharing improves area utilization, but on the other hand, this can lead to a performance degradation due to an increase in access conflicts. To mitigate the associated performance penalty, access interval prediction (AIP) has been proposed. In a similar fashion to branch prediction, AIP exploits program flow regularity to predict the cycle of the next memory access. We show that this structural similarity allows for adaption of state-of-the-art branch predictors, such as the TAgged GEometric history length (TAGE) branch predictor. Our analysis on memory access traces reveals that the obtained TAGE access interval predictor predicts over 97% of memory accesses outperforming all previously presented AIP schemes.