Abstract In this article, we describe four sequential sampling strategies for estimating the quantile of Y = f(X), where X has a known distribution in Rd and f is a deterministic unknown, expensive-to-evaluate real-valued function. These approaches all consist in modeling f as a sample of a well-chosen Gaussian process and aim at estimating the quantile by using as few evaluations of f as possible. The different methodologies are first compared through various numerical experiments. Then, in the framework of the ANR-JST FETUS project, we apply our strategies to a real example corresponding to the exposure of a Japanese pregnant-woman model and her 26-week-old fetus to a plane wave. Finally, we compare our methodologies on a simplified geometric model designed for modeling the fetus exposure to plane waves. Supplementary materials for this article are available online.