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Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a variant of the standard version (L1). How to control the intensity of accent is a very interesting research direction. Recent works design a speaker-adversarial loss to disentangle the speaker and accent information, and then adjust the loss weight to control the accent intensity. However, there is no direct correlation between the disentanglement factor and natural accent intensity. To this end, this paper proposes a new intuitive and explicit accent intensity control scheme for accented TTS. Specifically, we first extract the posterior probability from the L1 speech recognition model to quantify the phoneme accent intensity for accented speech, then design a FastSpeech2 based TTS model, named Ai-TTS, to take the accent intensity expression into account during speech generation. Experiments show that our method outperforms the baseline model in terms of accent rendering and intensity control.