Personalised computational models of the heart are of increasing interest for clinical applica- tions due to their discriminative and predictive abili- ties. However, the simulation of a single heartbeat with a 3D cardiac electromechanical model can be long and computationally expensive, which makes some practical applications, such as the estimation of model parame- ters from clinical data (the personalisation), very slow. Here we introduce an original multidelity approach between a 3D cardiac model and a simplied "0D" ver- sion of this model, which enables to get reliable (and extremely fast) approximations of the global behavior of the 3D model using 0D simulations. We then use this multidelity approximation to speed-up an ecient parameter estimation algorithm, leading to a fast and computationally ecient personalisation method of the 3D model. In particular, we show results on a cohort of 121 dierent heart geometries and measurements. Fi- nally, an exploitable code of the 0D model with scripts to perform parameter estimation will be released to the community.