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Secure aggregation, which is a core component of federated learning, aggregates locally trained models from distributed users at a central server, without revealing any other information about the local users' data. This paper follows a recent information theoretic secure aggregation problem with user dropouts, where the objective is to characterize the minimum communication cost from the K users to the server during the model aggregation. All existing secure aggregation protocols let the users share and store coded keys to guarantee security. On the motivation that uncoded groupwise keys are more convenient to be shared and could be used in large range of practical applications, this paper is the first to consider uncoded groupwise keys, where the keys are mutually independent and each key is shared by a group of S users. We show that if S is beyond a threshold, a new secure aggregation protocol with uncoded groupwise keys, referred to as GroupSecAgg, can achieve the same optimal communication cost as the best protocol with coded keys. The experiments on Amazon EC2 show the considerable improvements on the key sharing and model aggregation times compared to the state-of-the art.