Fitting parametric survival models with interval-censore d data is a common task in survival analysis and implemented in many statistical software pack ages. Here, we present a novel approach to fit such models if the values on the scale of intere st are measured with error. Random effects ANOVA models are used to account for the measurement errors and the likelihood function of the parametric survival model is maximized with numerica l methods. An illustration is provided with a real data set on the rejection of yogurt as a function of its acid taste. ; Peer Reviewed ; Postprint (published version)