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When mapping life expectancy, and investigating its local variation in time, there is a conflict between using large areas and/or mortality data from long periods of time to have low variance life expectancy estimates, and using small areas and single‐year mortality data to explore the space–time variation of life expectancy in detail, without bias. Here a Bayesian model is proposed to smooth annual small‐area life expectancy estimates and help deal with that trade‐off. The specific area effect on life expectancy, together with its spatial and temporal dependencies are modeled through random effects, while the effect of covariates is modeled through a fixed effect component. By smoothing life expectancy estimates directly, instead of smoothing age‐specific mortality rates first the way done in the literature, the model used is easier to implement and interpret. The approach is illustrated, by using it to explore how life …