We present a simple and quick method to estimate a network centrality measure. Our method, called QuickCent, is inspired in so called fast and frugal heuristics, which are heuristics initially proposed to model the human quantitative estimation process. The centrality index that we estimate is the harmonic index which is a measure based on shortest path distances, so infeasible to compute on large networks. We compare QuickCent with known machine learning algorithms on synthetic data. Our experiments show that QuickCent is able to make robust estimates compared with alternative methods achieving low-error variance estimates even with a small training set. Moreover, QuickCent is comparable in efficiency accuracy and time cost to more complex methods. Our initial results show that simple heuristics and biologically inspired computational methods are a promising line of research in the context of network measure estimations.