Rough set (RS) theory is an efficient technique for handling uncertain information, and one of the primary tasks in RS is to measure the uncertainty of knowledge. Considering that some existing information measures may not accurately quantify the uncertainty in certain situations. In addition, Such methods seem to be less explored for describing the physical meaning behind the functions. To address such issues, we defined a new entropy-based uncertainty measure named DE(X) in this paper. Some properties of the proposed DE(X) include the performance under the coarsest and finest division are investigated and numerical examples are illustrated to show the efficiency. The result shows that DE(X) more accurately measures the uncertainty under the roughest and finest division. The comprehensive advantages of the proposed DE(X) have been stated over the existing methods.