In this work, we examine a hybrid Monte-Carlo/deterministic approach for a toy model based on the parabolic time-dependent diffusion equation. We consider two different solvers: a low-cost "coarse" solver based on a deterministic Galerkin scheme and a "fine" solver based on a Monte-Carlo resolution. We use a hybrid "parareal-in-time" algorithm based on these two solvers to reduce the computational cost with respect to a full Monte-Carlo simulation. In a set of benchmark numerical experiments, we compare our hybrid parareal strategy with a standard full Monte-Carlo solution of the time-dependent diffusion equation. In particular, we show that for a large number of processors, our hybrid strategy significantly reduces the computational time of the simulation while preserving its accuracy. The convergence properties of the proposed Monte-Carlo/deterministic parareal strategy are also discussed.