OBJECTIVES:  Health utilities (HUs) are required to conduct cost-utility analyses (CUAs). Often, health-related quality of life (HRQOL) data, instead of HUs, are collected in clinical trials.  Increasingly, mapping algorithms have been developed to derive HUs from HRQOL data. However, the variance of the derived HUs based on mapping are observed to be smaller than those of the actual HUs. METHODS: Two reasons are proposed: (1) the presence of important unmeasured predictors leading to a high degree of unexplained variance of derived HUs, and (2) ignoring that the regression coefficients are random variables themselves. We derive three variance estimators of HUs to account for these reasons: (1) R2-adjusted estimator, (2) parametric estimator and (3) non-parametric estimator.  We tested these estimators using a simulated dataset and a real dataset involving EQ-5D and University of Washington Quality of Life questionnaire for patients with head and neck cancers. RESULTS:  The R2 adjusted estimator can be used in ordinary least square (OLS) based mapping algorithms and requires only the R2from the derivation study.  The parametric estimator can be used in OLS based mapping algorithms and requires the mean square error (MSE) and the design matrix from the derivation study.  The non-parametric estimator can be used in any mapping algorithm and requires leave-one-out cross-validation MSE from the derivation study.  In the simulated dataset, all three estimators are within 1% of the variance of the actual HUs.  In the real dataset, the unadjusted variance was 44% less than the actual variance, while all three estimators are within 10% of the actual variance. CONCLUSIONS: When conducting CUA based on mapping algorithms, the variance of derived HUs should be properly adjusted using one of the proposed methods so that the results of the CUA will have the appropriate degree of uncertainty.