Accurate, fast, and flexible approaches for contact angle estimation in molecular dynamics simulations are of great importance for characterization of surface wettability, especially for machine learning approaches which would usually require thousands of computational contact angle evaluations for training and prediction purposes. However, evaluation of the contact angle from molecular simulations is typically a human-intensive process, which hinders the required fast throughput. To address this challenge, here a flexible and automated contact angle estimation tool, ContactAngleCalculator, is developed to meet these new requirements. In contrast to the current widely used computational approaches that are laborious and human intensive, this code is based on the concepts of the coarse-graining technique and equivalent contact area and volume of the droplet. Once the parameters are determined for a target liquid, it can automatically estimate the contact angle of different time points of one case or multiple cases by only one click. This tool is targeted for integration with machine learning methods, in which it can substantially streamline and reduce human labor and time in a computational contact angle estimation.