A system can be considered to be reliable if it operates successfully for a long period of time and has fewer uncertainties during its lifespan. In other words, lower the uncertainty of a random variable implies higher reliability [see Ebrahimi N. How to measure uncertaintyin the residual life time distribution. Sankhya: Indian J Stat SerA. 1996;58:48–57]. Motivated by this, the present study considers a generalized entropy function, namely the Rényi entropy in the quantile framework as a measure of uncertainty and proposes two nonparametric estimators for its computation. Asymptotic properties of the estimators are established under suitable regularity conditions. Simulation study and real data analysis are carried out to compare the performance and usefulness of the estimators.