Contrail avoidance through flight deviations is thought to be the fastest way to reduce aviation-related warming. Nevertheless, uncertainty in contrail model accuracy makes it difficult to prioritize contrails for avoidance according to their predicted warming. To minimise the effect of model disagreement on contrail avoidance decisions, we reformulate the selection of candidate contrails for avoidance as an aggregation problem – a family of problems enabling joint decisions by several agents (contrail models) with different preferences. We compare several aggregation rules to determine whether using multiple contrail models can increase savings in the lifetime total extinction when compared to just using the most popular model, CoCiP. In the absence of a ground truth, we use the lifetime total extinction from a high-fidelity large eddy simulation (LES) as a proxy for the real warming. We test these aggregation strategies with two real models, and synthetic contrail model results (produced by applying a chosen functional bias and random noise to the reference results). For interpretability, we describe a contrail model as “moderately correlated” if its Pearson correlation coefficient relative to the reference lies between 0.50 and 0.60. We find that two separate aggregation strategies using two moderately correlated contrail models is expected to avoid 20 % more lifetime total extinction relative to using CoCiP by itself. This improvement rises to 33 % when using three moderately correlated contrail models. Since two aggregation strategies with different working principles achieve similar reductions in the lifetime total extinction, they may improve the efficacy of warming-based contrail avoidance.